Optimal impedance control for task achievement in the presence of signal-dependent noise
Optimal impedance control for task achievement in the presence of signal-dependent noise
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DOI：
10.1152/jn.00519.2003
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发表时间：
2004-08-01
影响因子：
2.5
通讯作者：
Kawato, M
中科院分区：
文献类型：
--
作者：
Osu, R;Kamimura, N;Kawato, M
存在无限多的阻抗参数值，并且因此存在不同的共收缩水平，其可以产生CNS必须从中选择一个的相似运动学。虽然信号依赖性噪声（SDN）预测更大的电机命令的变化在更高的共同收缩，阻抗和任务性能之间的关系在理论上是不明显的，因此在这里进行了检查。受试者进行目标导向的单关节肘关节运动，要么自然地移动到不同的目标尺寸，要么在不同的水平自愿共同合同。刚度通过先前提出的关节周围肌肉共同收缩指数（IMCJ）估计为整流EMG信号的加权和。当受试者向不同大小的目标移动时，IMCJ随着准确性要求的增加而增加，从而减少终点偏差。因此，不需要很大的准确性，受试者接受更差的性能与较低的共收缩。当受试者被要求增加共同收缩时，EMG和扭矩的变异性都增加，这表明神经运动命令中的噪声随着肌肉激活而增加。相比之下，最高IMCJ水平的最终位置误差最小。虽然共同收缩增加了电机命令噪声，这种噪声对任务性能的影响减少。受试者能够调节他们的阻抗和控制终点变化的任务要求的变化，他们没有自愿选择高阻抗，产生最小的终点误差。这些数据与基于SDN的理论的预测相矛盾，该理论假设仅终点方差最小化，因此需要对其进行修订。
There is an infinity of impedance parameter values, and thus different co-contraction levels, that can produce similar movement kinematics from which the CNS must select one. Although signal-dependent noise (SDN) predicts larger motor-command variability during higher co-contraction, the relationship between impedance and task performance is not theoretically obvious and thus was examined here. Subjects made goal-directed, single-joint elbow movements to either move naturally to different target sizes or voluntarily co-contract at different levels. Stiffness was estimated as the weighted summation of rectified EMG signals through the index of muscle co-contraction around the joint (IMCJ) proposed previously. When subjects made movements to targets of different sizes, IMCJ increased with the accuracy requirements, leading to reduced endpoint deviations. Therefore without the need for great accuracy, subjects accepted worse performance with lower co-contraction. When subjects were asked to increase co-contraction, the variability of EMG and torque both increased, suggesting that noise in the neuromotor command increased with muscle activation. In contrast, the final positional error was smallest for the highest IMCJ level. Although co-contraction increases the motor-command noise, the effect of this noise on the task performance is reduced. Subjects were able to regulate their impedance and control endpoint variance as the task requirements changed, and they did not voluntarily select the high impedance that generated the minimum endpoint error. These data contradict predictions of the SDN-based theory, which postulates minimization of only endpoint variance and thus require its revision.